Overview

Dataset statistics

Number of variables14
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 KiB
Average record size in memory112.7 B

Variable types

NUM13
CAT1

Reproduction

Analysis started2020-11-09 21:39:06.245801
Analysis finished2020-11-09 21:39:21.365670
Duration15.12 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Class
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2
71 
1
59 
3
48 
ValueCountFrequency (%) 
27139.9%
 
15933.1%
 
34827.0%
 
2020-11-09T18:39:21.419589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-09T18:39:21.472720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:21.535835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Alcohol
Real number (ℝ≥0)

Distinct126
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.00061798
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:21.630168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.811826538
Coefficient of variation (CV)0.06244522679
Kurtosis-0.8524995685
Mean13.00061798
Median Absolute Deviation (MAD)0.68
Skewness-0.05148233108
Sum2314.11
Variance0.6590623278
MonotocityNot monotonic
2020-11-09T18:39:21.735708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12.3763.4%
 
13.0563.4%
 
12.0852.8%
 
12.2942.2%
 
1231.7%
 
12.2531.7%
 
12.4231.7%
 
12.9321.1%
 
12.621.1%
 
12.8521.1%
 
Other values (116)14279.8%
 
ValueCountFrequency (%) 
11.0310.6%
 
11.4110.6%
 
11.4510.6%
 
11.4610.6%
 
11.5610.6%
 
ValueCountFrequency (%) 
14.8310.6%
 
14.7510.6%
 
14.3910.6%
 
14.3821.1%
 
14.3710.6%
 

MalicAcid
Real number (ℝ≥0)

Distinct133
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.336348315
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:21.844025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.117146098
Coefficient of variation (CV)0.478159053
Kurtosis0.2992066799
Mean2.336348315
Median Absolute Deviation (MAD)0.52
Skewness1.039651193
Sum415.87
Variance1.248015403
MonotocityNot monotonic
2020-11-09T18:39:21.947722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.7373.9%
 
1.8142.2%
 
1.6742.2%
 
1.6831.7%
 
1.6131.7%
 
1.5131.7%
 
1.3531.7%
 
1.5331.7%
 
1.931.7%
 
3.1721.1%
 
Other values (123)14380.3%
 
ValueCountFrequency (%) 
0.7410.6%
 
0.8910.6%
 
0.910.6%
 
0.9210.6%
 
0.9421.1%
 
ValueCountFrequency (%) 
5.810.6%
 
5.6510.6%
 
5.5110.6%
 
5.1910.6%
 
5.0410.6%
 

Ash
Real number (ℝ≥0)

Distinct79
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.366516854
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:22.049628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.5575
95-th percentile2.7415
Maximum3.23
Range1.87
Interquartile range (IQR)0.3475

Descriptive statistics

Standard deviation0.2743440091
Coefficient of variation (CV)0.1159273422
Kurtosis1.143978169
Mean2.366516854
Median Absolute Deviation (MAD)0.16
Skewness-0.1766993165
Sum421.24
Variance0.07526463531
MonotocityNot monotonic
2020-11-09T18:39:22.153090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.373.9%
 
2.2873.9%
 
2.763.4%
 
2.3663.4%
 
2.3263.4%
 
2.4852.8%
 
2.252.8%
 
2.3852.8%
 
2.542.2%
 
2.442.2%
 
Other values (69)12369.1%
 
ValueCountFrequency (%) 
1.3610.6%
 
1.721.1%
 
1.7110.6%
 
1.7510.6%
 
1.8210.6%
 
ValueCountFrequency (%) 
3.2310.6%
 
3.2210.6%
 
2.9210.6%
 
2.8710.6%
 
2.8610.6%
 

AlcalinityOfAsh
Real number (ℝ≥0)

Distinct63
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.49494382
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:22.264694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.339563767
Coefficient of variation (CV)0.171304098
Kurtosis0.4879415405
Mean19.49494382
Median Absolute Deviation (MAD)2.05
Skewness0.2130468864
Sum3470.1
Variance11.15268616
MonotocityNot monotonic
2020-11-09T18:39:22.516542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20158.4%
 
21116.2%
 
16116.2%
 
18105.6%
 
1995.1%
 
21.584.5%
 
18.573.9%
 
2273.9%
 
19.573.9%
 
22.573.9%
 
Other values (53)8648.3%
 
ValueCountFrequency (%) 
10.610.6%
 
11.210.6%
 
11.410.6%
 
1210.6%
 
12.410.6%
 
ValueCountFrequency (%) 
3010.6%
 
28.521.1%
 
2710.6%
 
26.510.6%
 
2610.6%
 

Magnesium
Real number (ℝ≥0)

Distinct53
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.74157303
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:22.630755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.28248352
Coefficient of variation (CV)0.1431948894
Kurtosis2.104991324
Mean99.74157303
Median Absolute Deviation (MAD)10
Skewness1.098191055
Sum17754
Variance203.9893354
MonotocityNot monotonic
2020-11-09T18:39:22.737610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
88137.3%
 
86116.2%
 
10195.1%
 
9895.1%
 
9684.5%
 
10273.9%
 
11263.4%
 
9463.4%
 
8563.4%
 
9752.8%
 
Other values (43)9855.1%
 
ValueCountFrequency (%) 
7010.6%
 
7831.7%
 
8052.8%
 
8110.6%
 
8210.6%
 
ValueCountFrequency (%) 
16210.6%
 
15110.6%
 
13910.6%
 
13610.6%
 
13410.6%
 

TotalPhenols
Real number (ℝ≥0)

Distinct97
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.29511236
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:22.841999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.6258510488
Coefficient of variation (CV)0.2726886317
Kurtosis-0.8356265234
Mean2.29511236
Median Absolute Deviation (MAD)0.505
Skewness0.0866385864
Sum408.53
Variance0.3916895353
MonotocityNot monotonic
2020-11-09T18:39:22.941044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.284.5%
 
363.4%
 
2.863.4%
 
2.663.4%
 
252.8%
 
2.9552.8%
 
1.3842.2%
 
1.6542.2%
 
2.4542.2%
 
2.8542.2%
 
Other values (87)12670.8%
 
ValueCountFrequency (%) 
0.9810.6%
 
1.110.6%
 
1.1510.6%
 
1.2510.6%
 
1.2810.6%
 
ValueCountFrequency (%) 
3.8810.6%
 
3.8510.6%
 
3.5210.6%
 
3.510.6%
 
3.410.6%
 

Flavanoids
Real number (ℝ≥0)

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.029269663
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:23.043463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.998858685
Coefficient of variation (CV)0.4922257023
Kurtosis-0.8803815472
Mean2.029269663
Median Absolute Deviation (MAD)0.835
Skewness0.02534355338
Sum361.21
Variance0.9977186726
MonotocityNot monotonic
2020-11-09T18:39:23.144328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.6542.2%
 
0.5831.7%
 
2.6831.7%
 
0.631.7%
 
1.2531.7%
 
2.0331.7%
 
0.9221.1%
 
0.6621.1%
 
2.4321.1%
 
2.9821.1%
 
Other values (122)15184.8%
 
ValueCountFrequency (%) 
0.3410.6%
 
0.4721.1%
 
0.4810.6%
 
0.4910.6%
 
0.521.1%
 
ValueCountFrequency (%) 
5.0810.6%
 
3.9310.6%
 
3.7510.6%
 
3.7410.6%
 
3.6910.6%
 

NonflavanoidPhenols
Real number (ℝ≥0)

Distinct39
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3618539326
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:23.238543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.1244533403
Coefficient of variation (CV)0.3439325349
Kurtosis-0.6371910641
Mean0.3618539326
Median Absolute Deviation (MAD)0.085
Skewness0.4501513356
Sum64.41
Variance0.01548863391
MonotocityNot monotonic
2020-11-09T18:39:23.335393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
0.26116.2%
 
0.43116.2%
 
0.29105.6%
 
0.3295.1%
 
0.384.5%
 
0.3784.5%
 
0.3484.5%
 
0.2784.5%
 
0.484.5%
 
0.2473.9%
 
Other values (29)9050.6%
 
ValueCountFrequency (%) 
0.1310.6%
 
0.1421.1%
 
0.1752.8%
 
0.1921.1%
 
0.221.1%
 
ValueCountFrequency (%) 
0.6610.6%
 
0.6342.2%
 
0.6131.7%
 
0.631.7%
 
0.5831.7%
 

Proanthocyanins
Real number (ℝ≥0)

Distinct101
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.590898876
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:23.433330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5723588627
Coefficient of variation (CV)0.3597707379
Kurtosis0.5546485226
Mean1.590898876
Median Absolute Deviation (MAD)0.38
Skewness0.5171371723
Sum283.18
Variance0.3275946677
MonotocityNot monotonic
2020-11-09T18:39:23.532810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.3595.1%
 
1.4673.9%
 
1.8763.4%
 
1.2552.8%
 
1.5642.2%
 
1.6642.2%
 
1.9842.2%
 
2.0842.2%
 
1.7731.7%
 
1.6331.7%
 
Other values (91)12972.5%
 
ValueCountFrequency (%) 
0.4110.6%
 
0.4221.1%
 
0.5510.6%
 
0.6210.6%
 
0.6421.1%
 
ValueCountFrequency (%) 
3.5810.6%
 
3.2810.6%
 
2.9610.6%
 
2.9121.1%
 
2.8131.7%
 

ColorIntensity
Real number (ℝ≥0)

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.058089882
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:23.635340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.318285872
Coefficient of variation (CV)0.4583322807
Kurtosis0.3815222728
Mean5.058089882
Median Absolute Deviation (MAD)1.51
Skewness0.868584791
Sum900.339999
Variance5.374449383
MonotocityNot monotonic
2020-11-09T18:39:23.741642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.642.2%
 
4.642.2%
 
3.842.2%
 
3.0531.7%
 
5.731.7%
 
2.931.7%
 
5.631.7%
 
531.7%
 
5.431.7%
 
4.531.7%
 
Other values (122)14581.5%
 
ValueCountFrequency (%) 
1.2810.6%
 
1.7410.6%
 
1.910.6%
 
1.9521.1%
 
210.6%
 
ValueCountFrequency (%) 
1310.6%
 
11.7510.6%
 
10.810.6%
 
10.6810.6%
 
10.5210.6%
 

Hue
Real number (ℝ≥0)

Distinct78
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9574494382
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:23.844495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.2285715658
Coefficient of variation (CV)0.2387296464
Kurtosis-0.3440957414
Mean0.9574494382
Median Absolute Deviation (MAD)0.165
Skewness0.0210912722
Sum170.426
Variance0.05224496071
MonotocityNot monotonic
2020-11-09T18:39:23.954115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.0484.5%
 
1.2373.9%
 
1.1263.4%
 
0.8952.8%
 
0.5752.8%
 
0.9652.8%
 
1.2552.8%
 
1.0542.2%
 
1.0942.2%
 
0.7542.2%
 
Other values (68)12570.2%
 
ValueCountFrequency (%) 
0.4810.6%
 
0.5410.6%
 
0.5510.6%
 
0.5621.1%
 
0.5752.8%
 
ValueCountFrequency (%) 
1.7110.6%
 
1.4510.6%
 
1.4210.6%
 
1.3810.6%
 
1.3621.1%
 

0D280/OD315
Real number (ℝ≥0)

Distinct122
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.611685393
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:24.057022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.7099904288
Coefficient of variation (CV)0.2718514376
Kurtosis-1.086434527
Mean2.611685393
Median Absolute Deviation (MAD)0.52
Skewness-0.307285499
Sum464.88
Variance0.5040864089
MonotocityNot monotonic
2020-11-09T18:39:24.154095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.8752.8%
 
342.2%
 
1.8242.2%
 
2.7842.2%
 
2.7731.7%
 
1.7531.7%
 
1.3331.7%
 
2.3131.7%
 
3.3331.7%
 
2.9631.7%
 
Other values (112)14380.3%
 
ValueCountFrequency (%) 
1.2710.6%
 
1.2921.1%
 
1.310.6%
 
1.3331.7%
 
1.3610.6%
 
ValueCountFrequency (%) 
410.6%
 
3.9210.6%
 
3.8210.6%
 
3.7110.6%
 
3.6910.6%
 

Proline
Real number (ℝ≥0)

Distinct121
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.8932584
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-11-09T18:39:24.257015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.9074743
Coefficient of variation (CV)0.4216231312
Kurtosis-0.2484031061
Mean746.8932584
Median Absolute Deviation (MAD)202.5
Skewness0.7678217814
Sum132947
Variance99166.71736
MonotocityNot monotonic
2020-11-09T18:39:24.357167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
68052.8%
 
52052.8%
 
63042.2%
 
62542.2%
 
75042.2%
 
51031.7%
 
56231.7%
 
66031.7%
 
45031.7%
 
103531.7%
 
Other values (111)14179.2%
 
ValueCountFrequency (%) 
27810.6%
 
29010.6%
 
31210.6%
 
31510.6%
 
32510.6%
 
ValueCountFrequency (%) 
168010.6%
 
154710.6%
 
151510.6%
 
151010.6%
 
148010.6%
 

Interactions

2020-11-09T18:39:07.704579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:07.812278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:07.899409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:07.990330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.084131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.169477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.259704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.342961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.427958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.517077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.603424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.688820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.774230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.863214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:08.944442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.091432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.169878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.252707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.325008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.400856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.470569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.542743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.617944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.690399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.763484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.836248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.911131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:09.997593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.076039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.159275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.246350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.322856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.404053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.479848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.557350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.638971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.717637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.795911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.874892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:10.954779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.043422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.124890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.210639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.301235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.381974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.467442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.622164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.702808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.789402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.872473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:11.955495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.038834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.124395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.201467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.270377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.345079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.422592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.489717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.561786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.628383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.697544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.771691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.841180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.909659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:12.978968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.051104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.133871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.208857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.289344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.372901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.445772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.523152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.595846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.670155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.748927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.824900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.900287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:13.974944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.052611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.128276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.195141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.268585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.345110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.412126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.481657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.638401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.705989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.777803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.846302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.915020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:14.982490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.053128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.130454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.198975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.273931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.352234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.420418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.491606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.559078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.627882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.700045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.771075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.841343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.912661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:15.985768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.069291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.148456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.229543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.313889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.389085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.468046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.541740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.617227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.696192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.772766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.848973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:16.925141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.003620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.082751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.153193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.229091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.309305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.379242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.452924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.521116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.591779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.665054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.735955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.807364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.879454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:17.952366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.030744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.101947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.290618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.371195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.441255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.516106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.585516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.656241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.731173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.803058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.874303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:18.945889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.018711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.098290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.168962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.247084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.327724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.398321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.472011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.541323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.612809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.687434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.760195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.832626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.905759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:19.981036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.063706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.139061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.219253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.302769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.375968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.454030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.526729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.600968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.679279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.754521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.829467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:20.905017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-09T18:39:24.459352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-09T18:39:24.623813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-09T18:39:24.784902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-09T18:39:24.946857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-09T18:39:21.066453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-09T18:39:21.277704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

ClassAlcoholMalicAcidAshAlcalinityOfAshMagnesiumTotalPhenolsFlavanoidsNonflavanoidPhenolsProanthocyaninsColorIntensityHue0D280/OD315Proline
0114.231.712.4315.61272.803.060.282.295.641.043.921065
1113.201.782.1411.21002.652.760.261.284.381.053.401050
2113.162.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
4113.242.592.8721.01182.802.690.391.824.321.042.93735
5114.201.762.4515.21123.273.390.341.976.751.052.851450
6114.391.872.4514.6962.502.520.301.985.251.023.581290
7114.062.152.6117.61212.602.510.311.255.051.063.581295
8114.831.642.1714.0972.802.980.291.985.201.082.851045
9113.861.352.2716.0982.983.150.221.857.221.013.551045

Last rows

ClassAlcoholMalicAcidAshAlcalinityOfAshMagnesiumTotalPhenolsFlavanoidsNonflavanoidPhenolsProanthocyaninsColorIntensityHue0D280/OD315Proline
168313.582.582.6924.51051.550.840.391.548.6600000.741.80750
169313.404.602.8625.01121.980.960.271.118.5000000.671.92630
170312.203.032.3219.0961.250.490.400.735.5000000.661.83510
171312.772.392.2819.5861.390.510.480.649.8999990.571.63470
172314.162.512.4820.0911.680.700.441.249.7000000.621.71660
173313.715.652.4520.5951.680.610.521.067.7000000.641.74740
174313.403.912.4823.01021.800.750.431.417.3000000.701.56750
175313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
176313.172.592.3720.01201.650.680.531.469.3000000.601.62840
177314.134.102.7424.5962.050.760.561.359.2000000.611.60560